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    • Hybrid machine learning model to predict 3D in-situ permeability evolution

    Metadata Updated: January 20, 2025

    Enhanced geothermal systems (EGS) can provide a sustainable and renewable solution to the new energy transition. Its potential relies on the ability to create a reservoir and to accurately evaluate its evolving hydraulic properties to predict fluid flow and estimate ultimate thermal recovery. Here we develop a hybrid machine learning (ML) model to predict permeability evolution of intermediate-scale (~10 m) hydraulic stimulation experiments at the Sanford Underground Research Facility (the EGS Collab project). We present a 3D map in situ permeability evolution for two of stimulation episodes in this project using microearthquakes (MEQs) data and injection histories of wellhead pressure and flow rate. This map includes both average reservoir permeability evolution over time and local fracture permeability distribution within the evolved reservoir. Compared with the ground truth of average permeability calculated from the well data, our predicted average permeability for these two episodes has a MSE value less than 2.9E-4 and R2 higher than 0.93, indicating that average permeability predicted by machine learning is consistent with good agreement with field observation. Additionally, distributed fracture permeability calculated by empirical equation over time shows the process of fracture propagation and identify the potential fluid path for geothermal reservoir.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: Creative Commons Attribution

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    Dates

    Metadata Created Date January 11, 2025
    Metadata Updated Date January 20, 2025

    Metadata Source

    Harvested from OpenEI data.json

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date January 11, 2025
    Metadata Updated Date January 20, 2025
    Publisher Pennsylvania State University
    Maintainer
    Identifier https://data.openei.org/submissions/7429
    Data First Published 2022-11-22T07:00:00Z
    Data Last Modified 2023-10-04T19:45:01Z
    Public Access Level public
    Bureau Code 019:20
    Metadata Context https://openei.org/data.json
    Metadata Catalog ID https://openei.org/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Data Quality True
    Datagov Dedupe Retained 20250120155001
    Harvest Object Id cf7787d1-76db-4d45-90cc-fd360476a8cf
    Harvest Source Id 7cbf9085-0290-4e9f-bec1-91653baeddfd
    Harvest Source Title OpenEI data.json
    Homepage URL https://gdr.openei.org/submissions/1311
    License https://creativecommons.org/licenses/by/4.0/
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    Program Code 019:006
    Projectlead Mike Weathers
    Projectnumber EE0008763
    Projecttitle Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties
    Source Datajson Identifier True
    Source Hash dcd935f109ab69589a9614967132d0b746c896ea281e069ca7313ec2e81f8907
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